Author(s): Rodrigo P. G. Bezerra; Bruno Melo Brentan; Ruben Imhoff; Andre Ferreira Rodrigues
Linked Author(s):
Keywords: Flood forecasting; Machine learning; QPF nowcasts; Radar rainfall; Hydrological modelling
Abstract: The increasing frequency and intensity of extreme rainfall events underscore the urgent need for reliable short-term flood forecasting systems. Machine learning (ML) models have demonstrated strong potential for data-driven hydrological prediction; however, their effective forecast lead time remains constrained by the catchment’s hydrological response, as rainfall information must propagate through the basin before discharge signals emerge. Integrating radar-based Quantitative Precipitation Forecasts (QPF), or nowcasting, offers a promising yet underexplored opportunity to extend ML-based flood forecast horizons. This study investigates the potential and limitations of using radar nowcasts as input for ML flood forecasting in the Geul River catchment—a steep 344 km² basin in the southern Netherlands that suffered catastrophic flooding in July 2021, causing over €200 million in local damages. The event exposed critical weaknesses in the operational forecast chain, particularly regarding the use of high-resolution rainfall predictions. Two baseline configurations are compared: (i) a forecast system using only observed rainfall up to time t, and (ii) a system combining observations up to t with “perfect” rainfall forecasts for t + T. This idealized framework quantifies the theoretical skill gain achievable through QPF integration, providing a benchmark for future hybrid flood forecasting systems.
Year: 2026